Accelerate efficiency gains with optimization and AI

Today, machine learning (ML), artificial intelligence (AI) and decision optimization (DO) are not just buzzwords you read in the press, but urgent requirements for any company that fears disruption and wants to do pragmatic analysis in order to make better decisions with their data. Data is the next natural resource but as with any resource, it’s not worth much until it helps you show results. Investment in AI can help accelerate and enhance those results.

A recent technical validation report by the Enterprise Strategy Group (ESG) mentioned that “improving operational efficiency” is the overarching theme driving interest in and adoption of AI/ML. And that’s no wonder, as organizations using optimization technology to make critical business decisions have seen millions of dollars in cost savings and other benefits such as improved customer service and lower inventory.

Many IBM clients are focused on accelerating data science projects and seeking ways to automate the AI lifecycle. They are also infusing prediction and optimization capabilities into decision-making and deploying enterprise AI across any cloud. For enterprises pursuing these goals, it’s essential to choose a future-proof architecture. That’s where IBM technology can make a real difference. For example, IBM Cloud Pak for Data is an open data and AI platform, designed as an integrated, fully governed multi cloud environment where organizations can keep data secure at its source and add preferred data and analytics microservices.

Furthermore, with IBM Watson Studio, organizations can capitalize on the power of prescriptive analytics using IBM Decision Optimization. This unique offering enables organizations to apply AI techniques to simplify the optimization modeling process, thereby reducing decision-making time. It’s also the rare enterprise AI offering that enables organizations to With it, data science teams can solve complex problems using optimization technology and machine learning within a unified environment.

Watson Studio helps data science teams choose between visual modeling tools — such as SPSS Modeler — and open source tools such as Python, R and Scala. Based on their available skill sets, businesses can use either intuitive design methods to build their algorithms or models, go with open source tools, or choose a mix of both.

What makes the IBM approach unique is not only its mix of open source languages and intuitive graphical tools, but also the APIs to other available IBM AI services such as Speech To Text, Tone Analyzer and Visual Recognition. This ecosystem empowers intuitive interactions with human users and access to all kinds of unstructured data, including speech, videos, photos and geospatial data.

With Watson Studio for IBM Cloud Pak for Data, IBM delivers a comprehensive answer for the needs of enterprises that want to drive innovation, improve operational efficiency and maximize growth. With it, data scientists are empowered to experiment and build minimum viable products (MVP) and proofs of concept (POC) in the public cloud through Watson Studio within IBM Cloud Pak for Data as a Service. And then when they are successful, they can move that success story to the internal private cloud on IBM Cloud Pak for Data — getting the same tool and same reliability with cloud-based elasticity, open source and Spark, all within the enterprise network.

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Sterling B2B Integrator and BPML

BPML, an XML-based language, serves to depict and execute business processes. Each business process is outlined in a distinct BPML document termed as a business process model (.bpml or .bp file). These models represent the blueprint of how processes operate within Sterling B2B Integrator.

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IBM Maximo Application Suite technical overview

A Maximo Application Suite instance operates within the Red Hat® OpenShift® Container Platform environment. This platform facilitates seamless migration between cloud and on-premises setups, as well as containerization and orchestration, ensuring durability, resilience, availability, and scalability. It also enables consistent deployments following the operator model, while automating and optimizing the development workflow.

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On April 21 2021, 11 AM CT